On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm

Evolutionary algorithms have been proven to be effective in solving multi-objective optimization problems. However, their performance deteriorates progressively in handling many-objective optimization problems due to the sensitivity upon the curvature of Pareto front, as well as the implicit evaluation on similarity in high dimensionality. This paper proposes an on-line Pareto front curvature estimator for an adaptive selection, in which the achievement scalarizing function is used to identify the pivotal solution to extrapolate the geometric information. Then an adaptive scalarizing function based fitness assessment, which guarantees the Pareto optimality, is presented. The diversity of the Pareto optimal solutions is also ensured by introducing a novel similarity metric. Finally, an extensive experimental analysis is presented to corroborate the analytical result by evaluating problems with various types of Pareto fronts. The experimental results substantiate the efficacy of the results with competitive performance. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes an online Pareto front curvature estimator and adaptive scalarizing functions to improve the performance of solving multi-objective optimization problems, while ensuring sensitivity to the Pareto front curvature and similarity evaluation in high dimensions. The diversity of Pareto optimal solutions is ensured by introducing a novel similarity metric.